Journal of Intelligent and Robotic Systems
Fuzzy-eta for Back Propagation Networks
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Robust Recurrent Neural Network Control of Biped Robot
Journal of Intelligent and Robotic Systems
An approach to beacons detection for a mobile robot using a neural network model
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
Fuzzy rules emulated network and its application on nonlinear control systems
Applied Soft Computing
An approach to beacons detection for a mobile robot using a neural network model
MS '07 The 18th IASTED International Conference on Modelling and Simulation
Stable Fourier neural networks with application to modeling lettuce growth
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A robust extended Elman backpropagation algorithm
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Adaptive controller with fuzzy rules emulated structure and its applications
Engineering Applications of Artificial Intelligence
On the weight convergence of Elman networks
IEEE Transactions on Neural Networks
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A robust backpropagation training algorithm with a dead zone scheme is used for the online tuning of the neural-network (NN) tracking control system. This assures the convergence of the multilayer NN in the presence of disturbance. It is proved in this paper that the selection of a smaller range of the dead zone leads to a smaller estimate error of the NN, and hence a smaller tracking error of the NN tracking controller. The proposed algorithm is applied to a three-layered network with adjustable weights and a complete convergence proof is provided. The results can also be extended to the network with more hidden layers